Repair of Exposed Ahmed Glaucoma Valve Tubes: Long-term Outcomes
Bibliographic record
Abstract
PURPOSE: To assess the long-term outcomes of glaucoma drainage device (GDD) exposure repair with corneal lamellar patch graft covered by a buccal mucous membrane graft sutured to surrounding conjunctiva. METHODS: In this retrospective longitudinal study, the charts of all patients who underwent buccal mucous membrane grafts combined with corneal lamellar patch grafts for exposed GDD tubes between the years 2006 to 2013 were reviewed. A minimum follow-up of 3 years was required for inclusion. Primary outcomes were categorized as complete success: adequate coverage throughout the study period without further intervention after 1 repair; qualified success: adequate coverage despite minor additional procedures (eg, suturing); failure: re-erosion of the GDD tube. RESULTS: A total of 23 tube exposures were included. Average time from GDD insertion to first erosion was 54.0±38.9 months (range, 5 to 120). Complete success was achieved in 19 cases (82.6%), and qualified success in 1 case (4.3%). There were 3 failures (13.1%). Overall success (complete+qualified) after 1 or 2 buccal mucous membrane graft repairs was achieved in 22 of 23 cases (95.7%). Average follow-up time for the successful cases (complete+qualified) was 69.5±25.4 months (median, 72.5; range, 36 to 124). There was no difference in time to first erosion between the success (complete+qualified) and failure groups: 53.8±38.3 months (range, 5 to 120) and 62.3±51.6 months (range, 5 to 105), respectively (P=0.78). CONCLUSIONS: Buccal mucous membrane grafts in combination with a lamellar corneal patch graft is a viable surgical strategy for eroded GDDs, providing good long-term outcomes; however, later interventions may be needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".